Evolutionary algorithms (EAs) have emerged as a powerful framework for
expensive black-box optimization. Obtaining better solutions with less
computational cost is essential and challenging for black-box optimization. The
most critical obstacle is figuring out how to effectively use the target task
information to form an efficient optimization strategy. However, current
methods are weak due to the poor representation of the optimization strategy
and the inefficient interaction between the optimization strategy and the
target task. To overcome the above limitations, we design a learned EA (LEA) to
realize the move from hand-designed optimization strategies to learned
optimization strategies, including not only hyperparameters but also update
rules. Unlike traditional EAs, LEA has high adaptability to the target task and
can obtain better solutions with less computational cost. LEA is also able to
effectively utilize the low-fidelity information of the target task to form an
efficient optimization strategy. The experimental results on one synthetic
case, CEC 2013, and two real-world cases show the advantages of learned
optimization strategies over human-designed baselines. In addition, LEA is
friendly to the acceleration provided by Graphics Processing Units and runs 102
times faster than unaccelerated EA when evolving 32 populations, each
containing 6400 individuals.

设计一种可学习的进化算法（LEA）来实现从手工设计的优化策略到学习的优化策略的转变，能够高效地利用目标任务的低保真信息来形成优化策略，以更少的计算成本获得更好的解决方案。